Darwin-27B-KR-V2
Qwen3.5 Hybrid Architecture | ~26B Params | Thinking Mode | 262K Context | BF16 | Apache 2.0
Model Overview
Darwin-27B-KR-V2 is the next evolution of FINAL-Bench/Darwin-27B-KR, enhanced with targeted Korean SFT (Supervised Fine-Tuning) for K-AI Leaderboard optimization.
Built on VIDRAFT's Darwin evolutionary merge lineage, this model inherits strong chain-of-thought reasoning from Darwin-27B-Opus and further refines Korean language capabilities through carefully curated training data targeting MuSR (Multi-Step Reasoning), KMMLU-Pro (Korean domain knowledge), and Metacognitive evaluation.
Key Features
- Darwin lineage — Evolutionary merge backbone from VIDRAFT's Darwin-27B-Opus
- K-AI targeted SFT — 1,027 curated Korean reasoning & knowledge pairs
- Thinking mode —
<think>tag based step-by-step reasoning - 262K context — Ultra-long document processing
- BF16 — Memory-efficient (~48GB)
- Apache 2.0 — Free for commercial use
Training
| Item | Details |
|---|---|
| Base Model | FINAL-Bench/Darwin-27B-KR |
| Method | LoRA SFT (rank=64, alpha=128) + Merge |
| Data | 1,027 Korean SFT pairs (MuSR 428 + KMMLU-Pro 500 + Metacognitive 99) |
| Epochs | 2 |
| Learning Rate | 2e-5 (cosine schedule) |
| Effective Batch | 16 |
| Target Modules | q/k/v/o_proj, gate/up/down_proj (1.17% trainable) |
| Hardware | 8x NVIDIA B200 (183GB each) |
| Training Time | ~25 minutes |
| Final Loss | 0.66 |
| Precision | BF16 |
SFT Data Composition
| Source | Count | Description |
|---|---|---|
| MuSR (Korean) | 428 | Multi-step reasoning: causal, temporal, spatial, counterfactual |
| KMMLU-Pro | 500 | Korean domain knowledge: law, economics, science, history, medicine |
| Metacognitive | 99 | Self-correcting reasoning with TICOS framework |
| Total | 1,027 | All pairs include <think> reasoning tags |
Model Specifications
| Property | Value |
|---|---|
| Architecture | Qwen3.5 (GatedDeltaNet Hybrid Attention, 64-layer) |
| Parameters | ~26B |
| Hidden Size | 5120 |
| Layers | 64 |
| Context Length | 262,144 tokens |
| Precision | BF16 (~48GB) |
| Vocab Size | 248,320 |
| Thinking | Supported (<think> tags) |
| License | Apache 2.0 |
VRAM Requirements
| Setup | VRAM | Notes |
|---|---|---|
| BF16 (native) | ~48 GB | Single H100/B200 or 2x A100 |
| 4-bit quantized | ~14 GB | Single RTX 4090 |
| 8-bit quantized | ~26 GB | Single A6000 |
Usage
Requirements:
transformers >= 4.57.0
Transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-27B-KR-V2")
model = AutoModelForCausalLM.from_pretrained(
"FINAL-Bench/Darwin-27B-KR-V2",
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [{"role": "user", "content": "대한민국 헌법재판소의 역할과 권한에 대해 설명해주세요."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
vLLM
vllm serve FINAL-Bench/Darwin-27B-KR-V2 \
--enforce-eager \
--max-model-len 32768 \
--dtype bfloat16
Lineage
Qwen/Qwen3.5-27B
|
v
FINAL-Bench/Darwin-27B-Opus (evolutionary merge by VIDRAFT)
|
v
FINAL-Bench/Darwin-27B-KR (Korean-specialized variant)
|
v
FINAL-Bench/Darwin-27B-KR-V2 (this model, + K-AI targeted SFT)
Acknowledgements
- VIDRAFT / FINAL-Bench — Darwin evolutionary merge system
- Qwen Team — Qwen3.5 architecture
Citation
@misc{darwin_27b_kr_v2_2026,
title = {Darwin-27B-KR-V2: Korean-Enhanced Reasoning Model},
author = {VIDRAFT},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-27B-KR-V2}}
}